When I think about AI search, I realize it’s more than just translating or localizing results. It’s about deciding which sources, narratives, and realities emerge on top. This complex system is incredibly fascinating to me, especially when I consider how multilingual regions like Catalonia challenge these AI search systems.
The unique geography of Catalonia, where Catalan and Spanish languages coexist, serves as an excellent stress test for AI technology. It’s intriguing to see the underlying patterns unfold when the same queries are entered in both languages across platforms like Google AI Overviews and ChatGPT.
In Catalonia, a query like Tradicions de Sant Jordi shows how AI systems can sometimes misidentify the language, often tagging Catalan as Occitan. This discovery was both surprising and revealing, shedding light on broader problems that transcend multilingual spaces.
Consider this: an AI system operating out of Barcelona with a local IP may choose the less prevalent language of Occitan over Catalan, a decision that feels bizarre given Catalonia’s linguistic and geographical context.
This issue isn’t isolated. In January 2023, Google acknowledged downgrading Catalan results in favor of Spanish, which sparked dissatisfaction among users. The subsequent updates improved things somewhat, but the root language-identification errors persist, affecting how AI synthesizes information today.
My journey into this topic has involved documenting AI search variations across Hispanic markets, observing how it often treats diverse Spanish-speaking regions as uniform, ignoring their unique contexts. However, in Catalonia, where geography remains constant, the retrieval patterns unfold in more distinct and educational ways.
For me, multilingual regions expose the foundational defaults in retrieval systems. Here, users can switch languages and observe firsthand how the system reallocates meaning, authority, and even the language of an answer.
The reality is, the same issues will likely emerge in seemingly monolingual markets, manifesting in different ways as AI technology advances.
Recently, I’ve been exploring the fascinating divergence in AI adoption between professional circles and general consumers. According to Datos and SparkToro’s latest data, this trend is becoming increasingly apparent.
It was intriguing to see how AI usage is starting to plateau among consumers while remaining on the rise in professional environments. Tools like Claude, ChatGPT, and Gemini are seemingly more popular in the B2B landscape.
Why we care. As I delve deeper into AI’s impact, it’s becoming clear that a universal AI strategy won’t work for everyone. It’s essential to identify whether my audience aligns with these broader trends or if their AI engagement habits are entirely different.
ChatGPT desktop growth slowed. From Fishkin’s analysis, it appears that ChatGPT’s usage in the U.S. has stagnated over recent months while Claude and Gemini continue their growth trajectories. It seems that professionals are continually finding value in these tools.
At its zenith, 37% of U.S. desktop users engaged with OpenAI or ChatGPT back in September 2025. This number dipped slightly to 34% by March, a trend mirrored, albeit with higher numbers, in the EU and U.K.
Claude gained with professionals. I noticed Claude is particularly gaining traction among professional users. Fishkin’s data suggests a significant rise in usage among business professionals, resonating with the notion that AI adoption is stronger in B2B contexts.
The analysis even revealed that Claude’s use among B2B professionals was 373% higher than the U.S. average, reinforcing the tool’s growing popularity in business circles.
Consumer audiences look different. Interestingly, when it comes to the retail-shopping consumer audience, ChatGPT isn’t as prevalent, being 15% less likely to be used compared to the typical American consumer. For this group, Claude isn’t even in the top four AI tools.
This might explain why AI seems so prevalent in professional networks like LinkedIn, while its visibility is not as pronounced among general consumers.
The research. You can view Rand Fishkin’s detailed insights on LinkedIn by watching his video here.
I’ve just delved into Goodie’s enlightening AI search traffic report for early 2026, covering the period from January to April, and I’m excited to share my insights with you. This report dives into trends in usership, referral traffic, and marketing considerations, offering a comprehensive view of the shifting landscape.
You’ll want to pay particular attention to how ChatGPT’s dominance is starting to wane, with some surprising contenders like Claude and Gemini making waves. This shift could significantly impact how marketers strategize their efforts in AI-driven search optimization.
The data reveals fascinating patterns in user habits and referral traffic, which could inform future marketing strategies and the allocation of resources. For a full dive into these emerging trends and what they might mean for businesses, I encourage you to explore the detailed findings of the report.
When I attended Google Marketing Live 2026, I witnessed firsthand how Gemini is reshaping the world of Search, advertising, commerce, and measurement. The event highlighted the move towards a more conversational, AI-driven ecosystem.
This year, the focus was on agentic AI, conversational Search, automated creative production, and AI-assisted shopping. Google rolled out tools across Search, YouTube, Merchant Center, and Analytics aimed at making campaigns more autonomous, predictive, and interconnected.
Let me take you through the biggest announcements from Google Marketing Live 2026.
Google Introduces a New Generation of AI-Powered Search Ads
Google rolled out new Gemini-powered ad formats that enhance AI Mode and conversational Search experiences.
The updates include:
Conversational Discovery ads
Highlighted Answers
AI-powered Shopping ads
Business Agent for Leads
These innovative formats are crafted to be more contextual and interactive by embedding AI-generated explanations and conversational experiences directly into Search journeys.
Plus, Google expanded its Direct Offers pilot with AI-generated bundles, native checkout, and travel promotions seamlessly integrated into AI-assisted Search experiences.
Google Launches Ask Advisor Across Ads, Analytics, and Merchant Center
At the event, Google introduced Ask Advisor, a Gemini-powered AI collaborator that bridges Google Ads, Analytics, Merchant Center, and the Google Marketing Platform.
It functions as a unified assistant to help marketers:
Build campaigns
Analyze performance
Receive recommendations
Automate operational tasks
Google assures that Ask Advisor expedites the process from planning to optimization by pulling insights across platforms.
Google Upgrades Measurement with Meridian and Predictive AI Tools
Google announced new tools for measurement and forecasting within Google Analytics 360.
Meridian, an open-source marketing mix model, is being integrated directly into Analytics 360, along with Qualified Future Conversions (QFCs), a predictive reporting metric powered by Gemini.
These tools will assist advertisers in:
Improving media mix modeling
Forecasting campaign outcomes
Measuring incrementality
Linking current ad activity with future revenue signals
I’m excited to share that Google is testing new conversational ad formats, powered by Gemini, across AI Mode and Search. This development is aimed at making ads more contextual and engaging, bringing a fresh approach to advertising.
The introduction of these Gemini-powered formats was revealed at Google Marketing Live 2026. With these new ad experiences, ads are intended to feel more conversational, contextually relevant, and genuinely helpful to users like you and me.
Driving the news: Google announced exciting additions to AI-powered Search ads. These include Conversational Discovery ads, Highlighted Answers, AI-powered Shopping ads, and the Business Agent for Leads. All these are part of Google’s strategy to integrate Gemini deeper into its Search and advertising framework.
Conversational Discovery ads are really innovative! Imagine asking a question about making your home smell like a spa, and right there in AI Mode, you see creative solutions generated with Gemini that perfectly match your query.
How it works: Google’s Gemini models analyze what you’re really asking and create ad content that fits the conversation. These ads come with an AI explainer that helps you understand the product or service better, integrating it with what the advertiser wants to tell you.
I’m particularly intrigued by the Highlighted Answers, where relevant ads pop up right within AI-generated recommendations. It feels like a natural extension of the conversation!
Additionally, Google is rolling out AI-powered Shopping ads for significant purchase decisions like buying a new TV or home appliance. Gemini steps in to create unique explainers that highlight why a product might be perfect for your needs.
Business Agent for Leads takes interactivity to a new level by embedding an AI chat experience in lead generation ads. Instead of completing static forms, you can chat with a Gemini-powered agent to learn more, directly informed by the sponsor’s website.
Moreover, Google is expanding its Direct Offers pilot, bringing features like promotion bundling, native checkout for UCP merchants, and AI-generated offer recommendations to the table. This ensures offers are tailored to what you might actually be shopping for!
Why we care: These updates represent a paradigm shift in how ads are rendered in AI-powered Search ecosystems. By focusing on conversational discovery and intent-rich interactions, I believe Google is paving the way for advertisers to better connect with their audiences.
It’s crucial for advertisers, who adapt quickly to these new ad formats, to optimize experiences that resonate better, potentially gaining an edge as user search habits evolve.
What to watch: As the rollout continues, I’ll be keeping an eye on how these conversational placements impact metrics like click-through rates and conversions. The broader implications for monetizing search with AI are enormous!
For those wondering when they can see these innovations: Conversational Discovery ads and Highlighted Answers are currently in testing phases in the U.S. on both mobile and desktop platforms. Meanwhile, AI-powered Shopping ads and the Business Agent for Leads feature are expected to unfold soon, starting in open beta for U.S. businesses.
Dig deeper: If you’re interested in more groundbreaking updates from Google Marketing Live 2026, check out these stories:
Entity optimization might sound like a complex term, but trust me, it’s incredibly powerful when you’re trying to make AI understand your brand better. Essentially, my goal is to help AI see exactly who I am and what I’m about. Let me share more about how you can do the same.
When I optimize entities related to my brand, I start by clarifying what my brand represents. This means ensuring that all my online content clearly reflects my brand’s identity and core values. By creating a strong, consistent message, AI can better understand and categorize my content.
Next, I focus on strengthening associations. This involves connecting my brand with relevant entities and concepts within my industry. When AI detects these connections, it increases my brand’s relevance in related searches.
Finally, driving accurate AI citations is crucial. I make sure that any references to my brand on different platforms are correct and consistent. This helps in building trust with AI, ensuring that it can reliably reference my brand in the right contexts.
I’m thrilled to share that Google has just unveiled Ask Advisor, a new AI-driven tool designed to transform the way we approach campaign management, analytics, and optimization. Announced at Google Marketing Live 2026, this Gemini-powered AI is here to integrate seamlessly across Google Ads, Google Analytics, Merchant Center, and the Google Marketing Platform.
Making Waves. Ask Advisor is set to be a game-changer, acting as a unifying force that weaves together insights, workflows, and recommendations across Google’s vast marketing ecosystem.
For those of us in marketing, this means we can launch campaigns, analyze performance, and uncover optimization recommendations all without having to juggle between different tools.
Imagine asking Ask Advisor to “find new customers for my hair care products.” It would seamlessly pull details from the Merchant Center and assist in crafting a campaign right in Google Ads.
Understanding the Process. Ask Advisor connects the dots between Google Ads, Analytics, the Merchant Center, and the Marketing Platform via a Gemini-powered interface. This connectivity allows it to access a range of data to create recommendations, automate tasks, and offer insights that align with marketing goals.
It doesn’t stop there. The integration of insights from Google Ads and Google Analytics helps explain campaign performance and suggests subsequent steps.
The aim, Google states, is to democratize advanced campaign management, enabling even those without extensive technical expertise to make the most out of their advertising strategies.
This launch supports Google’s expanding lineup of AI-driven in-product agents, positioning Gemini as a fundamental layer in advertising and measurement tools.
Why This Matters to Us. Ask Advisor symbolizes one of Google’s most direct steps into agent-based advertising workflows.
Instead of interacting manually with separate reporting dashboards, campaign tools, and optimization settings, AI agents are being poised to handle operational tasks and present strategic insights.
The more substantial evolution is structural: Google is anchoring Gemini as the core across its advertising platform, potentially redefining how campaigns are developed, optimized, and evaluated.
Keep an Eye On. The biggest discussion point will be how much control advertisers are willing to cede to AI agents. Transparency over recommendations, automation choices, and reporting accuracy will be under scrutiny as Ask Advisor rolls out.
When You Can Get It. Currently in beta, Ask Advisor is available for English-language accounts, with more features anticipated later this year.
Want to Learn More? Here’s additional news from Google Marketing Live 2026:
Today, I want to share some exciting news. Google has unveiled its most significant change to the search box in 25 years. This new feature, known as the “Intelligent Search Box,” is designed to provide us with an easier way to access AI search capabilities.
This innovation is powered by the latest technology, the Gemini 3.5 Flash.
Here’s How It Looks. Google completely redesigned the search box to give us more space for longer and deeper queries. As I type my search, the box will expand, supported by an AI-powered suggestion tool that goes beyond simple autocomplete, according to Google’s Head of Search, Liz Reid.
What’s even more impressive is the ability to search with text, images, files, videos, and even my Chrome tabs. It’s truly versatile!
Let me show you what this looks like:
This innovation puts Google’s most powerful AI tools right at our fingertips, enabling us to ask questions more easily, as explained by Liz Reid from Google.
Seamless Transition to AI Mode. Google also made it easier to switch to AI Mode with their new AI Overviews feature, which is now available globally on both desktop and mobile. Initially launched to many in January, it’s now fully operational.
Here’s how it works:
Why It Matters to Us. The transformation of the Google Search Box impacts how we search and potentially changes the type of traffic Google sends our way. It may encourage more users like me to switch to AI Mode for deeper answers, possibly leading to fewer direct clicks on our websites.
While change can be challenging, it’s also inevitable. Google’s CEO Sundar Pichai emphasized how our expectations from Google Search evolve—from individual queries to ongoing conversations and now to agentic workflows. As the world’s most-used product, Google is determined to stay ahead of our needs.
An analysis of 200 GPT-5.2 responses revealed that enhanced reasoning increases the citation of sources, deepens research, and boosts early-stage funnel visibility.
Subscribe to Growth Memo for weekly expert insights delivered straight to your inbox at no cost.
I’ve explored how AI provides a conversational experience through large language models (LLMs) and chatbots. However, I’ve noticed that no one has thoroughly examined the evolution of citations and mentions within these conversations.
By examining data from the Semrush AI Visibility Toolkit, I reviewed 20 buyer journeys across four industries, comparing the high and low reasoning of ChatGPT5.2.
In this analysis, you’ll discover:
How high reasoning cites a vastly different web with only 25.6% domain overlap and which source types gain or lose prominence.
The renewed importance of TOFU content: Brands cited at the Problem stage tend to persist through to the Selection stage under high reasoning.
How to differentiate your prompt tracking by reasoning modes, ensuring your AI visibility reports reflect two distinct systems instead of an average.
Methodology
Data collection utilized the Semrush AI Visibility Toolkit to capture prompts, citations, and fan-out queries generated by ChatGPT for each response.
We executed 100 prompts twice through GPT-5.2, once with minimal reasoning and once with high reasoning, totaling 200 responses.
Prompts covered 20 buyer journeys across four sectors (B2B SaaS, Finance, Consumer Tech, Health/Lifestyle), each consisting of 5 stages: Problem, Exploration, Comparison, Validation, Selection.
The citation rate represents the proportion of prompts where the response cited at least one external source.
The average citation quantifies the sources per cited response.
Fan-out queries are sub-queries the model generates internally for research before responding, surfaced via the Semrush API.
High Reasoning in GPT 5.2 Leads to More Citations and Searches
Activating high reasoning elevates the citation rate from 50% to 68%, nearly doubles the average sources per response (from 2.6 to 4.5), and multiplies fan-out queries by 4.6 times. High reasoning also draws from 173 unique domains versus 127 with minimal reasoning, with 99 domains appearing exclusively under high reasoning.
*Citation Rate signifies the share of prompts where at least one external source is cited.
This grounding is essential. When the model thinks more critically, it increasingly depends on web-based research, significantly impacting brand visibility, although user activation of reasoning remains uncertain.
Query intent provides a clearer indication than user demographics. Even free-tier users can access reasoning, albeit at limited rates, and ChatGPT automatically routes challenging prompts to Thinking mode. The critical question isn’t about affordability but about which prompts trigger reasoning automatically.
Complex comparisons, evaluation frameworks, compliance inquiries, and intricate shopping setups are most likely to invoke reasoning across all users. It’s crucial to categorize your audience by query type rather than paywall status.
High Reasoning Launches More Fan-out Queries in Later Stages
Users navigate problem-solving and purchasing decisions through stages, often within the same conversation. The distinction between minimal and high reasoning is not static; it varies based on the user’s journey stage.
For instance, consider a buyer evaluating CRM software:
Problem: “How do I know if my sales team needs a CRM?”
Exploration: “What types of CRM software exist for B2B SaaS?”
Comparison: “HubSpot vs. Salesforce vs. Pipedrive for a 50-person sales team.”
Validation: “Is HubSpot worth the price for mid-market B2B?”
Selection: “How do I get started with HubSpot Sales Hub?”
The following patterns are consistent across all 20 buyer journeys:
The citation rate increases as users progress through the funnel in both reasoning modes, but early-stage gaps close faster in high reasoning: +35pp at the Problem stage, only +5pp at Validation.
Fan-out queries peak during the Comparison stage, with high reasoning triggering 24 sub-queries per response compared to 5.5 in minimal reasoning. For Selection, these numbers are 15.4 and 2.6, respectively.
Average citations per response culminate during the Comparison stage (9.8 high, 5.8 minimal) and narrow during the Selection stage (4.7 high, 2.6 minimal). The citation pattern resembles an hourglass throughout the funnel.
Aggregately, minimal reasoning triggers 245 search queries over 100 prompts, while high reasoning triggers 1,130. In high reasoning, the model conducts thorough investigations for each prompt, with most research occurring during the Comparison and Selection phases.
What does fan-out look like?
A B2B SaaS prompt that requires high reasoning, like comparing Salesforce, HubSpot, and Pipedrive for a 50-person sales team, breaks down into different queries regarding API rate limits, compliance standards, support tools, pricing tiers, and more. Each aspect requires specific retrieval. The brand that succeeds here will be the one with clean, accessible documentation for each sub-query, not merely ranking for the initial prompt.
The Selection stage features a remarkable variance in per-response queries: between 0 and 40 fan-out queries with the same five-stage cohort. This variance is driven mainly by the specificity of prompts.
Bounded prompts (like “should I finance through the dealer at 0% APR or use a bank?” or “draft an RFP to 3 SEO agencies”) run zero queries since the answer’s structure is predefined. On the other hand, open-ended tasks (“shopping list for a $3,000 home gym” or “which travel card system matches our grocery spending?”) prompt 28 to 40 queries. With no single query type dominating the Selection stage, the model’s research intensity correlates with the degrees of freedom left by the prompt.
For marketers: Capturing early-funnel visibility is highly dependent on reasoning mode. If buyers engage with ChatGPT in reasoning mode, your Problem-stage and Exploration-stage content become more relevant. Otherwise, visibility might only surface during the Comparison stage.
How Reasoning Alters Brand Representation in Conversations
A session with an LLM is more conversational than transactional. Does an initially cited brand endure till the concluding stage? If yes, early-funnel visibility multiplies. If no, each step is an independent battleground.
For minimal reasoning, persistence from the Problem stage to the Selection stage rarely happens. With high reasoning, however, continuous brand presence was recorded in 4 journeys across all 5 stages.
Within individual responses, high reasoning strongly relies on specific sources, with 51 out of 100 high-reasoning responses citing the same domain multiple times versus 26 in minimal reasoning. When committed, high reasoning cites a source repeatedly.
Analyzing brand names mentioned in the text provides a broader perspective. With a relaxed test criterion, persistence was noticeable in 3 high-reasoning sessions and 2 in minimal reasoning: HubSpot through CRM Selection, American Express in Business Credit Cards, and prominent mentions of Sony and Canon in Mirrorless Cameras. Consumer Tech again emerges, albeit without citation persistence, showing dominance through continuous conversation presence.
High reasoning establishes a consistent perception of the solution landscape throughout a session. Crucially, TOFU prompts possess enormous value. A brand appearing at the Problem stage is likely to be present at the Selection stage. Top-of-funnel content transcends mere brand awareness for AI visibility—it’s a predictor of where the model’s reasoning lands at decision-making points.
There are two more significant insights:
All four persistent journeys occur within Finance, indicating persistence thrives on authoritative-source content like regulatory pages and official brand sites, echoing the +28pp lift in Finance.
For marketers focusing on account-based strategies or market creation, visibility in reasoning mode is paramount as it’s the sole mode turning early funnel efforts into selection-stage citations.
Reasoning Mode: A Distinct Search Paradigm
The champions under minimal reasoning differ from those under high reasoning: Three out of four cited domains diverge. The diversity in source types and citation stages is unmistakable.
I’m particularly intrigued by these findings:
Firstly, measurement. It’s imperative to differentiate low and high reasoning in our prompt trackers to avoid oversimplification, as their functions are distinct.
This endeavor may seem costlier, but it significantly enhances prompt tracking accuracy.
Secondly, the relevance of funnel stages. In the latest AI Mode user behavior study, it was observed that users heavily rely on shortlists, much like they do with Google’s top results. It initially appeared that focusing on BOFU prompts to generate shortlists was most strategic.
Nonetheless, TOFU prompts carry substantial benefits due to their persistence potential. Brands entering the buyer journey early can remain present throughout. Mapping buyer journeys and tracking persistence offer the best insights.
This post originally appeared on the author’s website and is reproduced here with permission.
In my journey to optimize AI search visibility, I’ve discovered some of the best tools in Generative Engine Optimization (GEO). These tools not only boost citations in platforms like ChatGPT and Gemini but also guide me in selecting the most effective GEO platform for my needs.
Let me show you how you can measure AI search visibility effectively. It’s all about understanding how your content interacts with these advanced systems and using the right tools to enhance your reach.
Choosing the right GEO platform can be a game-changer. It’s essential to select a system that aligns perfectly with your goals and optimizes your AI-driven content for maximum impact.
